An improved ant colony optimization algorithm for robotic path planning
一种改进的机器人路径规划的蚁群算法

An improved ant colony optimization(ACO) algorithm for robotic path planning in a complex roundabout environment is proposed. The adaptive migratory probability function is introduced to make ants have the ability to travel in forward and backward direction of the target; thus, the ability in finding circuitous routes is improved. The distance elicitation factor and the crossing obstacle detection mechanism are introduced into the visibility information to integrate the path search with the obstacle-avoiding process for improving the search efficiency. The greedy pheromone updating strategy and the node pheromone distribution mode are studied to optimize the path planning result, convergence rate and data storage. The simulation results validate the effectiveness of the algorithm.